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BIJ
18,5 Benchmarking for investment
decisions: a case of food
production
694
Anatoliy G. Goncharuk
Department of Management and Finance,
Odessa National Academy of Food Technologies, Odessa, Ukraine
Abstract
Purpose – The paper aims to focus on improving the methodology and developing the model of
choice of optimal investment object using benchmarking tools that eliminate the drawbacks of existing
approaches.
Design/methodology/approach – The methodological basis of the proposed model is frontier
analysis, namely the nonparametric data envelopment analysis. Using this and other benchmarking
tools, the author introduces the concept and mathematical model for evaluation of super-attractiveness
for investors that allows a full ranking of potential objects for investment.
Findings – The concept of variable investment decision that combines various periods, varying
degrees of risk and other decision characteristics with a common purpose of maximizing the benefits
from investments is defined. The model for the making of variable investment decisions is developed.
Practical implications – The proposed model enables strategic and portfolio investors to
implement the optimal choice of investment object. It is demonstrated on a case of the food production
of Ukraine.
Originality/value – This paper adopts benchmarking tools to the decision-making process to
optimal choice of investment object.
Keywords Benchmarking, Investment attractiveness, Super-efficiency, Investment decision,
Data Envelopment Analysis (DEA), Food production, Investments, Food industry
Paper type Research paper
Introduction
As the previous study (Goncharuk, 2009a) shows, benchmarking detects the best
practices, factors and reserves for performance improvement. These features of
benchmarking can be useful not only for enterprise managers and owners, but for
potential investors which look for an optimal way for investing. Making the right
decision about where to invest is an important objective of any investor. Depending on
the purpose of investing, investment decisions may be different: from the portfolio that
is aimed at earning the expected returns at the lowest acceptable risk, to strategic with
long-term goals of ownership and governance of the investment object. Such solutions
can differ not only in goals, but also in the degree of risk, amounts and forms of
investment, etc. Considering the totality of such decisions, we define the concept of
variable investment decisions as decisions of the investor on the investing of financial
Benchmarking: An International resources for various periods in objects with varying degrees of risk and other
Journal characteristics, but with the common purpose – to maximize the benefits from these
Vol. 18 No. 5, 2011
pp. 694-704 investments. In our opinion, the determining factor of this decision should be the result
q Emerald Group Publishing Limited of the selection process for the object of investment by the established criteria.
1463-5771
DOI 10.1108/14635771111166820 The financial and economic crisis has shown that existing methodological approaches

2. to selecting investment targets are ineffective. Its primary disadvantages are the Benchmarking
limited purposes, static character, focus only on financial indicators, large number for investment
of used factors and the complexity of interpreting of the results. In our opinion, the
benchmarking tools can eliminate many of these shortcomings. decisions
Thus, this study focuses on improving the methodology and developing the model
of choice of optimal investment object using benchmarking tools that eliminate the
drawbacks of existing approaches. To examine this model, we consider a case of 695
Ukrainian food industry in order to find the optimal enterprises for investing.
Investment attractiveness of enterprises’ potential is usually used in the science and
practice as a criterion for choosing the optimal investment object (enterprise). The
scientific literature does not develop a common approach to the definition of this
concept. Zahorodniy and Voznyuk (2008) and Nosova (2007) define it as a generalized
description of the advantages and disadvantages of investing in certain areas and
objects from the perspective of a particular investor. Others consider the investment
attractiveness more simply – as the expediency of free capital investments in an
enterprise (Rusak and Rusak, 1997), or more comprehensively as an integral feature
of individual enterprises as objects of future investments from the prospects of
development of production and sales, efficient use of assets, their liquidity, the state of
solvency and financial stability (Bryukhovetska and Khasanova, 2009).
Summarizing the existing treatments, we defined the concept of “investment
attractiveness of enterprises” as characteristics describing the system of integrated
indicators of expediency of investments in a company, which reflects the totality of
existing conditions and factors that promote or hinder the process of investing.
There are different opinions on a choice of the methodology and model to measure
an investment attractiveness. Traditionally, investors use two criteria for choosing
between capital investment projects – the net present value (NPV) and the internal rate
of return (IRR). They often provide inconsistent rankings. This inconsistency is hotly
debated about which criterion is better. The debate has lasted more than a century.
Some explorers (Osborne, 2010) suggest new methods of calculating for NPV, the
other (Kierulff, 2008) modify IRR. However, the suggestions to determine the level of
investment attractiveness of enterprises and create an adequate rating with a single
indicator are debatable. Practice (Bennouna et al., 2010) confirms both the widespread
use of NPV and IRR and making of poor decisions based on them only.
There are multi-criteria approaches to solving this problem. Dudka (2006) considers
a system of statistically significant indicators as the most appropriate method for this.
This system should include a general indicator and several levels of interrelated
individual indicators, which fully characterize the object under investigation and have
a common dimension and structure. Blank (2001) suggests that you first determine the
stage of the life cycle of the company, which will evaluate its investment attractiveness.
Balatsky (2004) and the other scholars tend to use the expert-rating systems for the
evaluation of investment attractiveness, which are widespread in developed countries.
All these approaches have their disadvantages. Practice of using them in the financial
crisis leads to a distortion of reality and making suboptimal decisions related to
investments in this or that object.
Most of the existing methodological approaches to the measurement of investment
attractiveness of company are poor or include many heterogeneous indicators and ratios,
which can hardly be perceived as a whole and on the basis of which it is practically

3. BIJ impossible to provide real prospects of the development of a company and its
environment. Therefore, in our view, it is necessary to develop a special model and pay
18,5 more attention to the most important aspects of any enterprise – efficiency and
profitability. It is a reliable estimate of the efficiency of the company and its growth
potential that can protect investors from the risk of loss of funds. The joint evaluation
of investment attractiveness and the relative efficiency of its activity makes it possible
696 to take into account the situation of enterprises in the environment and the prospects
for its development.
Methodology
Under existing conditions of limited investment resources, we offer a model for
selecting investment targets based on the three-level approach, including the consistent
application of inter-industry, intra-industry and corporate analysis of investment
attractiveness, efficiency, and profitability.
The methodological basis of proposed model is frontier analysis, namely the
nonparametric method called Data Envelopment Analysis (hereinafter referred to as
DEA) that was for the first time proposed by Charnes et al. (1978) and then has received
extensive theoretical development and practical application in various spheres of
human activity over the past three decades. DEA is the usage of linear programming
methods for constructing nonparametric piecewise surfaces (frontier) according to the
data of enterprises of sample, and calculation of efficiency index concerning this
surface (Coelli et al., 2005). DEA is now one of the most popular tools for performance
measurement and benchmarking in the various fields, for example, in manufacturing
(Goncharuk, 2009a), power generation and distribution (Farzipoor Saen, 2010;
Goncharuk, 2008), transportation (Abraham George and Rangaraj, 2008),
communication (Mitra Debnath and Shankar, 2008), trade ( Joo et al., 2009), medicine
(Lambert et al., 2009), etc.
This study uses DEA super-efficiency model by Anderson and Petersen (1993)
for complete ranking (hierarchy) of the enterprises of sample as to the efficiency.
We propose to modify this model to assess the relative investment attractiveness
and ranking of companies on its level. In this case, the obtained model of the
super-attractiveness for investor (SIA) can be mathematically expressed as follows:
X
n
min a sup ; subject to : vj xij þ s2 ¼ a sup xiq ; i ¼ 1; 2; . . . ; m
i
j¼1;–q
ð1Þ
X
n
vj yrj 2 sþ ¼ yrq ; r ¼ 1; 2; . . . ; s vj ; s2 ; sþ $ 0;
r i r
j¼1;–q
where, a sup – the value of super-attractiveness of object (companies, industry); x and
y – the values of inputs and outputs of the model, respectively; s2 – the deviation of
i
input of the ith type of the frontier; sþ – the deviation of output parameter of
r
the rth type of frontier; vj – weights; m – number of inputs, r – number of outputs,
n – number of objects.
The practical application of this model (1) consists in the possibility of obtaining the
ratings of the level of relative investment attractiveness of each object (enterprise,
industry) of the sample. By analogy with the measurement of effectiveness, we offer

4. to take the denominators of the basic indicators of investment attractiveness as inputs, Benchmarking
i.e. material resources, as well as their numerators as outputs, i.e. financial results. for investment
A set of basic indicators should reflect the current rate of return on investment in
the operating activities of the object, its capital goods and financial risk of investment decisions
in the object. As inputs, we propose to use the following indicators: total operating
costs, depreciation of fixed assets, and total liabilities. As outputs, we use the net sales
and net working capital. 697
For the ranking of objects in terms of efficiency, in our view, it is advisable to use
the DEA super-efficiency model with major operating cost items as inputs (material
costs, wages, depreciation, and miscellaneous costs) and net sales as an output. In order
to rank the level of profitability, we offer to use the return on total assets.
The two-dimensional graphic comparison of investment attractiveness and
efficiency will enable a strategic investor (SI) to select the best objects for investment.
It is necessary to make such ranking and comparison of at least two periods in order to
make a deliberate decision on assessing trends in the analyzed characteristics of objects.
We offer to use the Malmquist total factor productivity index (MPI) (Goncharuk, 2007)
for identifying common trends in the efficiency of sampling and site specific. This index
introduced by Caves et al. (1982) is derived for general production structures. MPI defines
the fundamental characteristic of a productivity index as a ratio between an output
quantity change index and an input quantity change index (Bjurek, 1996). It characterizes
the general changes between the two periods of technical efficiency and technological
developments that involve the development of new products and technologies that enable
the rapid growth of output compared with an increase in consumption of resources.
In our view, this is an important determinant of business prospects that influence the
decision making on strategic investment in the object. Moreover, the return to scale may
be an important factor of the success of strategic business development. Its estimation
for selected enterprises of analyzed sample would be indicating the level of desirable
expansion of production (business) in terms of efficiency.
To make a decision about portfolio investment, we offer graphic comparison of the
evaluation of investment attractiveness and profitability as portfolio investors (PI) are
more interested in these characteristics. To increase the validity of decisions, it is
appropriate to evaluate and compare these characteristics during at least two intervals
in order to assess trends of their changes.
Model
We offer the model for making the variable investment decisions (MVID model)
particularly for SIs and PI that are based on benchmarking tools. The essence of its
phases is outlined below.
At the first stage, we offer to rank the industries (activities) in which an investor
wishes to invest. For the SI, it is advisable to use the rating that is built on the basis of
super-efficiency estimates (SE) obtained by means of using an appropriate DEA
super-efficiency model by Anderson and Petersen (1993). To improve the reliability of
the choice, it is better to make this rating for the two periods and select one of the
leading industries. For PI, we suggest using the rating of industries that is built on the
basis of estimates of returns on assets, on equity capital or product profitability.
Making such rating of two periods will make it possible to identify the industry with
the most stable and high profitability.

5. BIJ The first phase of the MVID model detects one or two sectors for potential
18,5 investment.
The second stage consists in intra-industry analysis of investment attractiveness.
Its main goal is to choose one or two companies of each of the industries, which were
selected at the previous stage, in which it is expedient to invest. The initial stage of the
MVID model consists in estimating SIA scores using the model (1). For the SI, we offer
698 to compare SIA scores with SE scores for each selected company (industry) in the
two-dimensional coordinate system. By means of separating the resulting distribution
of enterprises in four quadrants by analogy with the efficiency-profitability matrix by
Dyson et al. (1990), we will select just that group of companies, which covers the upper
right quadrant called area of attractiveness for a SI. Such procedure is desirable to be
carried out during the interval of two periods, in order to avoid appearance of
accidental “stars” and reduce the risk of investing. We also offered to evaluate MPIs for
each of the selected enterprises and for the whole sample of enterprises for each of the
selected industries. The best index will indicate the highest rate of efficiency growth
and will be one of the determinants of the decision making of SI.
At this stage, MVID model offers the PI to compare the SIA scores with the
profitability of assets for enterprises of the sample in two-dimensional coordinate
system. Companies which are in the upper right quadrant called area of attractiveness
for PI for two periods are potential targets for portfolio investments.
The third stage consists in an in-depth analysis of financial condition of selected
companies and evaluating their opportunities of implementation of institutional
arrangements for the acquisition of their shares. SI should:
.
study the composition and structure of investments of existing shareholders
(owners);
. evaluate their own financial capabilities;
.
formulate investment proposals; and
.
negotiate with major owners the possibility of purchasing their stake in the
business or buying the additional issue of shares.
PI should be in the stock market for shares of selected enterprises. In case of a closed
form of business organization, an investor should negotiate the possible occurrence of
the shareholders of the company with the owners.
Thus, the MVID model enables SI and PI to make an informed choice of investment
objects (companies) and provide affiliation to its owners.
Case study
In order to demonstrate the practical aspects of using the proposed model, we will
make a careful study of its work by the example of the food industry in Ukraine.
Taking into consideration the importance to society and the growing demand for food
products, food industry has always been important for investors from different
countries (Skripnitchenko and Koo, 2005; Makki et al., 2004). Our calculations are dated
2006 and 2008.
First stage. Rating was formed for the four-digit NACE items of economic activities
(industries) that enter into the composition of the food industry. The results of the top
of the SE score rating for the food industries of Ukraine are given in Table I.

6. This rating indicates that the manufacture of beer is the most attractive for Benchmarking
investors among the Ukrainian food industries. It is this industry that becomes the for investment
object for further analysis.
Second stage. While analyzing the investment attractiveness of the beer industry, decisions
it should be noted that over 90 percent of the industry belongs to four major producers:
SUN InBev Ukraine, Baltic Beverages Holding Ukraine, Company “Obolon” and Sarmat
Brewery Company. The rest of the market is divided among dozens of small companies. 699
Thus, you need either major investments (hundreds of millions US$) or relatively small
investments (few millions US$) in order to enter this market as SI. The sample of 25 beer
companies in Ukraine for a period of two years has been analyzed. The SIA estimates
received with the help of the model (1) for this sample are presented in Table II.
Despite the general deterioration in the performance of the beer industry in 2008 due
to the influence of economic and financial crisis and other negative factors, some of the
leading enterprises of the industry in terms of investment attractiveness, including
Khmelpivo, Sarmat Brewery Company and BNC Radomyshl hold their positions
confidently. It should be also highlighted that the loss of the relative investment
attractiveness of one of the market leaders Company “Obolon” and the significant
improvement of the position of SUN InBev Ukraine (it has risen to third place in the
SIA rating in 2008) took place.
We compared the SIA estimates with the SE in two-dimensional coordinate system
for two years in order to select the best companies for SI (Figure 1).
Comparison shown that if in 2006 the area of attractiveness for SI were only three
enterprises beer industry (Khmelpivo, Sarmat Brewery Company and Uman brewery),
then in 2008 due to changes in the relative efficiency and investment attractiveness this
area was included already six companies, namely: Khmelpivo, SUN InBev Ukraine,
Ohtyrka brewery, Lviv brewery, Uman brewery and Bershad brewery. Estimates of
the MPI and return to scale for these companies are presented in Table III.
Given that all the selected companies from the area of attractiveness are based on
industry efficiency frontier, they have constant returns to scale, i.e. increase in inputs
leads to a proportional increase in output. Therefore, this condition can be considered
equal to them.
Low values of MPI for the “Lviv brewery”, SUN InBev Ukraine and Ohtyrka brewery,
which is much lower than one, indicate the negative dynamics of total factor
productivity in these companies. Thus, they should be excluded from further
consideration in terms of optimality for SI. The other selected companies (Table III),
despite the impact of financial crisis, have a very positive dynamics of total productivity,
which is a definite advantage in favour of their selection as a target for the SI.
Four-digit economic activities Super-efficiency score (%) No. in rating
15.96 Manufacture of beer 119.4 1
15.11 Manufacture of meat 117.5 2
15.91 Manufacture of distilled alcohol beverages 115.8 3
15.93 Manufacture of wine 93.5 4
15.13 Manufacture of meat products 86.7 5 Table I.
... ... ... Ranking of the economic
activities by
Source: Goncharuk (2009b) super-efficiency

7. BIJ
2006 2008
18,5 Number in SIA score Number in SIA score Change of
Company name rating (%) rating (%) rating position
Khmelpivo 1 628.7 1 819.4 –
Sarmat Brewery Company 2 252.3 2 345.1 –
700 Company “Obolon” 3 229.5 15 94.0 212
BNC Radomyshl 4 97.6 4 145.9 –
Uman brewery 5 91.6 7 109.6 22
Chernyatinske pyvo 6 83.2 24 42.8 218
Imperia-S 7 80.2 14 95.2 27
Lviv brewery 8 76.8 6 127.8 þ2
BNC Slavutich 9 65.2 9 101.6 –
Dnepropetrovsk brewery “Dnipro” 10 64.2 10 100.8 –
SUN InBev Ukraine 11 63.1 3 175.3 þ8
Brovar 12 59.4 12 97.9 –
Bershad brewery 13 59.0 8 102.0 þ5
Ohtyrka brewery 14 56.8 11 100.2 þ3
“Poltavpivo” firm 15 53.8 20 52.2 25
Rovenki brewery 16 52.4 13 97.5 þ3
Cherkaske Pyvo 17 52.3 25 21.0 28
Riven’ 18 48.3 16 80.4 þ2
Brewery on Podol 19 48.0 5 142.2 þ 14
Novograd-Volynskiy brewery 20 47.3 23 48.1 23
Opillya 21 46.0 22 48.8 21
Table II. Sevastopol brewery 22 44.1 17 74.7 þ5
Scores and ranking of Zahidpyvo 23 43.8 18 60.7 þ5
investment attractiveness Izyum brewery 24 43.2 21 50.7 þ3
of Ukrainian breweries Pavlivskiy brewery 25 30.2 19 53.2 þ6
2006 2008
3.2 3.2
2.8 Area of 2.8 Area of
attractiveness for attractiveness for
2.4 strategic investor 2.4 strategic investor
Super-efficiency
Super-efficiency
2 2
1.6 1.6
Figure 1. 1.2 1.2
Comparison of
super-investment 0.8 0 1 2 3 4 5 6 7 8 9 0.8 0 1 2 3 4 5 6 7 8 9
attractiveness and
super-efficiency for 0.4 0.4
Ukrainian breweries for
2006 and 2008 0 0
SIA SIA

8. To select the best companies for PI, we compared the SIA estimates with the Benchmarking
profitability of assets for two years (Figure 2). for investment
The carried-out comparison showed that whereas in 2006 only two companies of
beer industry (Khmelpivo and Company “Obolon”) got the area of attractiveness for PI, decisions
in 2008, due to changes in the level of profitability and investment potential,
already five companies were in this area, namely: Khmelpivo, SUN InBev Ukraine,
Ohtyrka brewery, Uman brewery and Bershad brewery. But only Khmelpivo were 701
stably attractive and profitable during the analyzed period, hence portfolio
investments in this company are the least risky among the companies of the industry.
Third stage. In-depth financial analysis of selected companies indicates the
following:
.
Uman brewery and Ohtyrka brewery do not have their own circulating capital.
These companies fund both the turnover and a substantial part of fixed assets by
loans; hence, they cannot be considered as reliable objects for investment.
.
SUN InBev Ukraine, in spite of the profitable operation and high investment
attractiveness, had negative dynamics in both productivity and profitability,
the latter fell for two years from 10.6 to 2.2 percent. This does not allow PI to
guarantee the necessary efficiency of investments. Besides, this company is
practically in private ownership of the largest foreign investor and the purchase
of its share may be difficult.
Company name Malmquist TFP index Return to scale
Khmelpivo 1.722 Constant
Uman brewery 1.478 Constant Table III.
Lviv brewery 0.871 Constant Malmquist TFP index
SUN InBev Ukraine 0.563 Constant and return to scale for
Bershad brewery 1.439 Constant selected Ukrainian
Ohtyrka brewery 0.786 Constant breweries
2006 2008
30 30
Profitability of assets (%)
Profitability of assets (%)
0 0
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9
Figure 2.
–30 –30 Comparison of
super-investment
Area of attractiveness Area of attractiveness attractiveness and
for portfolio investor for portfolio investor profitability of assets for
Ukrainian breweries for
–60 –60 2006 and 2008
SIA SIA

9. BIJ .
Khmelpivo and Bershad brewery are the optimum for both the SI and PI.
18,5 Both companies are highly profitable, efficient, with the positive dynamics of
total factor productivity. However, due to the organizational and legal form of
Bershad brewery (it is a closed joint stock company), portfolio investments into
this company are difficult, and opportunities for SI depend on the results of
negotiations with the major owner of Company “Obolon”. Taking into
702 consideration the size of selected companies, amounts of funds for strategic and
portfolio investments are relatively small (within one to two million US$); hence,
they are accessible to many potential investors.
The demonstrated case shows how an investor can find the desired object for
investment and make a balanced variable investment decision based on the results of
benchmarking and comprehensive analysis in the result of the phased implementation
of the proposed MVID model.
Conclusions
Benchmarking makes investment decisions more grounded and optimal. Studying of
the methodological aspects and practical problems that arise in the result of grounding
and making different investment decisions, allowed the author to elaborate a number
of innovations:
(1) definition of the concept of variable investment decisions that are the decisions of
an investor on the investing of financial resources for various periods in objects
with varying degrees of risk and other characteristics, but with a common
purpose – to maximize the benefits from these investments;
(2) introduction of the concept and mathematical model for evaluation of
super-attractiveness for investor that allows make a full ranking of potential
objects for investment; and
(3) development of the model for MVID model that is based on benchmarking tools.
The MVID model has important practical significance and allows strategic and
PI to implement the optimal choice of investment object. The work and
effectiveness of the proposed model are demonstrated on the case of the food
industry of Ukraine.
The MVID model is quite versatile and can be applied for making investment decisions
in other industries, not only for food production. Future directions for research on this
issue will be associated with empowerment of the model and its application to other
fields.
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Corresponding author
Anatoliy G. Goncharuk can be contacted at: agg@ua.fm
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